Language is one of the primary means by which we describe the 3D world around us. While rapid progress has been made in text-to-2D-image synthesis, similar progress in text-to-3D-shape synthesis has been hindered by the lack of paired (text, shape) data. Moreover, extant methods for text-to-shape generation have limited shape diversity and fidelity. We introduce TextCraft, a method to address these limitations by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs for training. TextCraft achieves this by using CLIP and using a multi-resolution approach by first generating in a low-dimensional latent space and then upscaling to a higher resolution, improving the fidelity of the generated shape. To improve shape diversity, we use a discrete latent space which is modelled using a bidirectional transformer conditioned on the interchangeable image-text embedding space induced by CLIP. Moreover, we present a novel variant of classifier-free guidance, which further improves the accuracy-diversity trade-off. Finally, we perform extensive experiments that demonstrate that TextCraft outperforms state-of-the-art baselines.
We describe our work on inferring the degrees of freedom between mated parts in mechanical assemblies using deep learning on CAD representations. We train our model using a large dataset of real-world mechanical assemblies consisting of CAD parts and mates joining them together. We present methods for re-defining these mates to make them better reflect the motion of the assembly, as well as narrowing down the possible axes of motion. We also conduct a user study to create a motion-annotated test set with more reliable labels.